Deep Learning and Artificial Intelligence Engineering Bootcamp in Barcelona Code School

Online 1:1 mentored bootcamp for building real machine learning and AI skills

Learn step by step how modern ML and AI systems are built

Learn how to build and understand ML systems, not just use them as black boxes.
Study with 1:1 mentor guidance, support, and a flexible part-time format.
Move from Python and classical ML into deep learning, transformers, and modern AI.

FORMAT

Remote, mentored

OUTCOME

Junior ML/AI engineering readiness

LANGUAGE

English

DURATION

13 weeks, part-time

Tools

Python
Jupyter
Matplotlib
Pandas
Scikit-Learn

What you'll learn

Artificial Intelligence development with BERT and GPT
Language models and transformers
Artificial, convolutional, recurrent, and generative neural networks
Machine learning, tree models, and K-means clustering
Advanced programming with Python
Data loading, data wrangling, and data visualization

Support designed to keep you progressing

13-week part-time mentored format
Text and video materials to study on a flexible schedule
Regular 1:1 sessions with your instructor
Support platform for questions and feedback
Industry-recognized graduation certificate

The outcomes

After the bootcamp you will be ready to start applying for junior roles such as:

Artificial Intelligence Engineer
Deep Learning Engineer
Natural Language Processing Engineer
Machine Learning Engineer
Data Scientist
Data Engineer
Computer Vision Engineer
Voice and Audio Processing Engineer

How to join the bootcamp

Choose your starting date and register online with the 900€ prepayment.
Fill out the student form.
Receive the prep materials.
Join our Slack workspace.
Start preparing before the bootcamp begins.
Make the remaining payment due 5 days before the starting date.
On day 1 receive the workflow instructions and main materials to start working on the course.

Your mentor

Dr. Minerva Singh

Minerva holds a PhD from the University of Cambridge, an MPhil from the School of Geography and Environment, and an MSc from the Department of Engineering at Oxford University.

With more than a decade of academic research and mentoring experience, she brings strong practical knowledge in machine learning, deep learning, data visualization, spatial data analysis, and natural language processing with R and Python.

She has contributed to peer-reviewed journals including PLOS One, reviewed for journals such as Remote Sensing, and given guest lectures at events like Open Data Science Conference.

Curriculum breakdown

Module 1: Python

Reinforce Python fundamentals and learn two essential tools for data work: Pandas and Matplotlib.

By the end of this module you will be able to load data, explore it, clean it, and visualize it in different ways.

Module 2: Classical ML, Tree-Based Models, K-Means Clustering and more

Learn core machine learning models that every deep learning engineer and data scientist should know, with both theory and practice.

Module 3: Intro to Neural Networks

Get introduced to neural networks and deep learning.

Learn gradient descent, back propagation, and optimization methods for deep learning.

Project suggestion: start working with the MNIST handwritten digit database.

Module 4: Recurrent Neural Networks and Convolutional Neural Networks

Work with convolutional and recurrent neural networks and visual or text analysis techniques.

Project suggestions: spam recognition, sentiment analysis, text classification, object detection, background detection, object counting, and tracking.

Module 5: Generative Neural Networks (GANs and VAEs)

Start working with more advanced concepts such as GANs and VAEs and learn to implement selected models from scratch.

Project suggestions: image colorization, text-to-image synthesis, noise reduction, and pose generation.

Module 6: Language Models and Transformers

Learn about transformers, transfer learning, and model tuning, and understand why these models perform the way they do.

You will implement “Attention is All You Need” and other selected papers.

Project suggestions: chatbot and object detection with vision transformers.

Module 7: Final Project and Intro to Modern AI

Develop your own unique project or follow one of the prepared tasks while working with topics like BERT, GPT, diffusion models, NeRFs, data annotation, and deployment.

Project suggestions: object video tracking, text-to-audio, depth extraction from image, and translation chatbot.